Video content consumers may wish to identify prominent persons, actors, and/or characters in the video content. Consuming video content, however, involves a significant time commitment. Unlike photos, which may be consumed instantly, a user may need to view an entire video before identifying important persons, actors, and/or characters. For example, if a user has a video collection that includes hundreds of video files, he or she may need to watch an entire length of a video file in the video collection to identify prominent persons, actors, and/or characters in the video file. Ultimately, the user may need to watch the entire video collection to identify which video files are associated with particular prominent persons, actors, and/or characters. Because such an identification process is time consuming, techniques have been developed to optimize video consumption.
Current techniques for identifying characters in video content involve face detection and grouping. However, many of the current techniques produce low quality results and/or are not accurate because of differences between image data and video content. In image data, persons, actors, and/or characters generally pose during the image data capture. Accordingly, the persons, actors, and/or characters are typically still (e.g., free of motion) and lighting conditions are uniform. However, in the context of video content, persons, actors, and/or characters generally do not pose and the quality of video content is less uniform than image data. For example, many times persons, actors, and/or characters are in motion and accordingly, may be facing away from the camera. In some situations, the persons, actors, and/or characters change facial expressions or may be partially occluded. Lighting conditions in video content vary such that recognizing persons, actors, and/or characters is more difficult than in image data. Accordingly, current techniques are insufficient for efficiently and effectively identifying important persons, actors, and/or characters in video data.